Performance Index for Water Distribution Networks under Multiple Loading Conditions
Bibliographic record
Abstract
Previous studies have naturally related water distribution network performance to the ability to deliver sufficient pressure and flow. The present paper emphasizes that performance also depends on the efficiency of delivering these requirements. Accordingly, an efficiency-based performance index is proposed. It is the geometric average of four performance metrics: reliability, vulnerability, resilience, and connectivity. These are themselves based on the energy efficiency, hydraulic capacity, and structural ability of the system to deliver water under a range of conditions. The metrics are applied to two example networks and variations of these, enabling the assessment of their relevance, their sensitivity to system changes, and permitting a comparison to existing metrics. Variations represent different redundancy increasing strategies, recognized for improving performance. The proposed performance index generally follows a similar trend as the previous indices, increasing with network pressure. Nevertheless, it varies differently and penalizes networks with unnecessarily high pressures. Because the index is based on energy and demand efficiency metrics, it automatically complies with the energy and mass balances of the network. Moreover, the new metric is easily interpreted and can be applied to various systems, whether complex or involving multiple scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".